Glasgow TB ACF

1. Libraries and functions

1.1 Libraries

Load the required libraries.

library(tidyverse)
library(sf)
library(here)
library(readxl)
library(scales)
library(DT)
library(brms)
library(tidybayes)
library(patchwork)
library(marginaleffects)
library(ggrepel)
library(scico)
library(ggdensity)
library(ggpubr)
library(units)
library(glue)
library(ggh4x)

1.2 Helper functions

Functions that we will use throughout the script

#labeller for years
year_labels <- c(1950:1963)

#The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
#Segment for graphs to match ACF period
acf_start <- decimal_date(ymd("1957-03-11"))
acf_end <- decimal_date(ymd("1957-04-12"))

Function for counterfactual plots

plot_counterfactual <- function(model_data, model, population_denominator, outcome, grouping_var=NULL, re_formula,...){
  
  #labeller for years
  year_labels <- c(1950:1964) #extra year for the extant of the x-axis

  #The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
  #Segment for graphs to match ACF period
  acf_start <- decimal_date(ymd("1957-03-11"))
  acf_end <- decimal_date(ymd("1957-04-12"))

  summary <- {{model_data}} %>%
    select(year, year2, y_num, acf_period, {{population_denominator}}, {{outcome}}, {{grouping_var}}) %>%
    add_epred_draws({{model}}, re_formula={{re_formula}}) %>%
    group_by(year2, acf_period, {{grouping_var}}) %>%
    mean_qi() %>%
    mutate(.epred_inc = .epred/{{population_denominator}}*100000,
          .epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
          .epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
    mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                  acf_period=="c. post-acf" ~ "Post Intervention"))



  #create the counterfactual (no intervention), and summarise
  
  counterfact <-
    add_epred_draws(object = {{model}},
                    newdata = {{model_data}} %>%
                                  select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, {{outcome}}) %>%
                                  mutate(acf_period = "a. pre-acf"), re_formula={{re_formula}}) %>%
    group_by(year2, acf_period, {{grouping_var}}) %>%
    mean_qi() %>%
    mutate(.epred_inc = .epred/{{population_denominator}}*100000,
         .epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
         .epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
    mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))
  


  #plot the intervention effect
p <- summary %>%
    droplevels() %>%
    ggplot() +
    geom_vline(aes(xintercept=acf_start, linetype="Mass CXR screening intervention")) +
    geom_vline(aes(xintercept=acf_end, linetype="Mass CXR screening intervention")) +
    geom_ribbon(aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
    geom_ribbon(data = counterfact %>% filter(year>=1956), 
                aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
    geom_line(data = counterfact %>% filter(year>=1956), 
              aes(y=.epred_inc, x=year2, colour="Counterfactual")) +
    geom_line(aes(y=.epred_inc, x=year2, group=acf_period,  colour=acf_period)) +
    geom_point(data = {{model_data}} %>%
                 mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Pre-ACF",
                                               acf_period=="b. acf" ~ "ACF",
                                               acf_period=="c. post-acf" ~ "Post-ACF")), 
               aes(y={{outcome}}, x=year2, shape=fct_relevel(acf_period,
                                                             "Pre-ACF",
                                                             "ACF",
                                                             "Post-ACF")), size=2) +
    theme_grey() +
    scale_y_continuous(labels=comma, limits =c(0,400)) +
    scale_x_continuous(labels = year_labels,
                       breaks = year_labels) +
    scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:", na.translate = F) +
    scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:", na.translate = F) +
    scale_shape_discrete(name="Empirical data (period):", na.translate = F) +
    scale_linetype_manual(values = 2, name="") +
    labs(
      x = "",
      y = "CNR (per 100,000)"
    ) +
    guides(x = "axis_truncated", y = "axis_truncated") +
    theme(legend.position = "bottom",
          legend.box="vertical", 
          text = element_text(size=10),
          axis.text.x = element_text(size=10, angle = 90, hjust=1, vjust=0.5),
          legend.text = element_text(size=10),
          legend.spacing.y = unit(0.1, 'cm'),
          axis.line = element_line(colour = "black")) 

    facet_vars <- vars(...)

  if (length(facet_vars) != 0) {
    p <- p + facet_wrap(facet_vars)
  }
  p

}

Function for calculating measures of change over time (RR.peak, RR.level, RR.slope)

summarise_change <- function(model_data, model, population_denominator, grouping_var = NULL, re_formula = NULL) {
  
  #functions for calculating RR.peak
  #i.e. relative case notification rate in 1957 vs. counterfactual trend for 1957
  
  grouping_var <- enquo(grouping_var)
  
  if (!is.null({{grouping_var}})) {
    
    #make the prediction matrix, conditional on whether we want random effects included or not.
    out <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num == 8),
                    acf_period = c("a. pre-acf", "b. acf")
    )
  } else {
    
    out <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num == 8),
                    acf_period = c("a. pre-acf", "b. acf")
    )
  }
  
  peak_draws <- add_epred_draws(newdata = out,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
    mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
    group_by(.draw, !!grouping_var) %>%
    summarise(estimate = last(epred_cnr)/first(epred_cnr)) %>%
    ungroup() %>%
    mutate(measure = "RR.peak")
  
  peak_summary <- peak_draws %>%
    group_by(!!grouping_var) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.peak")
  
  
  #functions for calculating RR.level
  #i.e. relative case notification rate in 1958 vs. counterfactual trend for 1958
  
    if (!is.null({{grouping_var}})) {
    out2 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num == 9),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  } else {
    
    out2 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num == 9),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  }
  
    level_draws <- add_epred_draws(newdata = out2,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
    arrange(y_num, .draw) %>%
    mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
    group_by(.draw, !!grouping_var) %>%
    summarise(estimate = last(epred_cnr)/first(epred_cnr)) %>%
    ungroup() %>%
    mutate(measure = "RR.level")
  
  level_summary <- level_draws %>%
    group_by(!!grouping_var) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.level")
    
    
  #functions for calculating RR.slope
  #i.e. relative change in case notification rate in 1958-1963 vs. counterfactual trend for 1959-1963
  
    if (!is.null({{grouping_var}})) {
    out3 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  } else {
    
    out3 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  }
  
    slope_draws <- add_epred_draws(newdata = out3,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
        arrange(y_num) %>%
        ungroup() %>%
        mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
        group_by(.draw, y_num, !!grouping_var) %>%
        summarise(slope = last(epred_cnr)/first(epred_cnr)) %>%
        ungroup() %>%
        group_by(.draw, !!grouping_var) %>%
        summarise(estimate = last(slope)/first(slope)) %>%
        mutate(measure = "RR.slope")
  
  slope_summary <- slope_draws %>%
     group_by(!!grouping_var) %>%
      mean_qi(estimate) %>%
      mutate(measure = "RR.slope")
    
  #gather all the results into a named list
    lst(peak_draws=peak_draws, peak_summary=peak_summary, 
        level_draws=level_draws, level_summary=level_summary, 
        slope_draws=slope_draws, slope_summary=slope_summary)
  
}

Function for calculating difference from counterfactual

calculate_counterfactual <- function(model_data, model, population_denominator, grouping_var=NULL, re_formula=NA){
  
  #effect vs. counterfactual
  counterfact <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}) %>%
                                    mutate(acf_period = "a. pre-acf"),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, year, {{grouping_var}}, acf_period) %>%
      mutate(.epred_inc_counterf = .epred/{{population_denominator}}*100000, .epred_counterf=.epred)  %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, {{population_denominator}}, .draw, .epred_counterf, .epred_inc_counterf, {{grouping_var}})
  
  #Calcuate case notification rate per draw, then summarise.
  post_change <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, acf_period),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, year, {{grouping_var}}, acf_period) %>%
      mutate(.epred_inc = .epred/{{population_denominator}}*100000)  %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, {{population_denominator}}, {{grouping_var}}, .draw, .epred, .epred_inc, {{grouping_var}}) 
  
  #for the overall period
    counterfact_overall <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}) %>%
                                    mutate(acf_period = "a. pre-acf"),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, {{grouping_var}}) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select({{population_denominator}}, .draw, .epred, {{grouping_var}})  %>%
      group_by(.draw, {{grouping_var}}) %>%
      summarise(.epred_counterf = sum(.epred)) 
  
  #Calcuate case notification rate per draw, then summarise.
  post_change_overall <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, acf_period),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, {{grouping_var}}) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select({{population_denominator}}, {{grouping_var}}, .draw, .epred) %>%
      group_by(.draw, {{grouping_var}}) %>%
      summarise(.epred = sum(.epred)) 
  
  
counter_post <-
  left_join(counterfact, post_change) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf,
           diff_inc100k = .epred_inc - .epred_inc_counterf,
           rr_inc100k = .epred_inc/.epred_inc_counterf) %>%
    group_by(year, {{grouping_var}}) %>%
    mean_qi(cases_averted, pct_change, diff_inc100k, rr_inc100k) %>%
    ungroup()

counter_post_overall <-
  left_join(counterfact_overall, post_change_overall) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf) %>%
    group_by({{grouping_var}}) %>%
    mean_qi(cases_averted, pct_change) %>%
    ungroup()

lst(counter_post, counter_post_overall)

}

Function for tidying up counterfactuals (mostly for making nice tables)

tidy_counterfactuals <- function(data){
  data %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(year = as.character(year),
            cases_averted = glue::glue("{cases_averted} ({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change} ({pct_change.lower} to {pct_change.upper})"),
            diff_inc = glue::glue("{diff_inc100k} ({diff_inc100k.lower} to {diff_inc100k.upper})"),
            rr_inc = glue::glue("{rr_inc100k} ({rr_inc100k.lower} to {rr_inc100k.upper})"))
}


tidy_counterfactuals_overall <- function(data){
  data %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(year = as.character(year),
            cases_averted = glue::glue("{cases_averted} ({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change} ({pct_change.lower} to {pct_change.upper})"))
}

2. Data

Import datasets for analysis

2.1 Shapefiles

Make a map of Glasgow wards

glasgow_wards_1951 <- st_read(here("mapping/glasgow_wards_1951.geojson"))

Reading layer `glasgow_wards_1951' from data source 
  `/Users/petermacpherson/Documents/Documents - Peter’s Mac mini/Projects/Historical TB ACF 2023-11-28/Work/analysis/glasgow-cxr/mapping/glasgow_wards_1951.geojson' 
  using driver `GeoJSON'
Simple feature collection with 37 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -4.393502 ymin: 55.77464 xmax: -4.070411 ymax: 55.92814
Geodetic CRS:  WGS 84

#read in Scotland boundary
scotland <- st_read(here("mapping/Scotland_boundary/Scotland boundary.shp"))

Reading layer `Scotland boundary' from data source 
  `/Users/petermacpherson/Documents/Documents - Peter’s Mac mini/Projects/Historical TB ACF 2023-11-28/Work/analysis/glasgow-cxr/mapping/Scotland_boundary/Scotland boundary.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 5513 ymin: 530249 xmax: 470332 ymax: 1220302
Projected CRS: OSGB36 / British National Grid

#make a bounding box for Glasgow
bbox <- st_bbox(glasgow_wards_1951) |> st_as_sfc()

#plot scotland with a bounding box around the City of Glasgow
scotland_with_bbox <- ggplot() +
  geom_sf(data = scotland, fill="antiquewhite") +
  geom_sf(data = bbox, colour = "#C60C30", fill="antiquewhite") +
  theme_void() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA, linewidth = 0.5),
        panel.background = element_rect(fill = "#EAF7FA", size = 0.3))

Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
Please use the `linewidth` argument instead.

#plot the wards
#note we tidy up some names to fit on map
glasgow_ward_map <- glasgow_wards_1951 %>%
  mutate(ward = case_when(ward=="Shettleston and Tollcross" ~ "Shettleston and\nTollcross",
                          ward=="Partick (West)" ~ "Partick\n(West)",
                          ward=="Partick (East)" ~ "Partick\n(East)",
                          ward=="North Kelvin" ~ "North\nKelvin",
                          ward=="Kinning Park" ~ "Kinning\nPark",
                          TRUE ~ ward)) %>%
  
  ggplot() +
  geom_sf(aes(fill=division)) +
  geom_sf_label(aes(label = ward), size=3, fill=NA, label.size = NA, colour="black") +
  #scale_colour_identity() +
  scale_fill_brewer(palette = "Set3", name="City of Glasgow Division") +
  theme_grey() +
  labs(x="",
       y="",
       fill="Division") +
  theme(legend.position = "top",
        
        panel.border = element_rect(colour = "grey78", fill=NA, linewidth = 0.5),
        panel.background = element_rect(fill = "antiquewhite", size = 0.3),
        panel.grid.major = element_line(color = "grey78")) +
  guides(fill=guide_legend(title.position = "top", title.hjust = 0.5, title.theme = element_text(face="bold")))

#add the map of scotland as an inset
glasgow_ward_map + inset_element(scotland_with_bbox, 0.75, 0, 1, 0.4)

ggsave(here("figures/s1.tiff"), height=10, width = 12)

NA
NA

3. Denominators

Load in the datasets for denonomiators, and check for consistency.

overall_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "overall_population")

overall_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
overall_pops <- overall_pops %>%
  mutate(year2 = year+0.5)

Note, we have three population estimates:

1. Population without institutionalised people or people in shipping
2. Population in institutions
3. Population in shipping

(Population in shipping is estimated from the 1951 census, so is the same for most years)

3.1 Overall population

First, plot the total population

overall_pops %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2), alpha=0.5, colour = "mediumseagreen", fill="mediumseagreen") +
  geom_point(aes(y=total_population, x=year2), colour = "mediumseagreen") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(
    title = "Glasgow Corporation: total population",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist()

NA
NA

Now the population excluding institutionalised and shipping population

overall_pops %>%
  ggplot() +
  geom_area(aes(y=population_without_inst_ship, x=year2), alpha=0.5, colour = "purple", fill="purple") +
  geom_point(aes(y=population_without_inst_ship, x=year2), colour = "purple") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(
    title = "Glasgow Corporation: population excluding institutionalised and shipping",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist()

NA
NA

3.2 Population by Ward

There are 5 Divisions containing 37 Wards in the Glasgow Corporation, with consistent boundaries over time.

#look-up table for divisions and wards
ward_lookup <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "divisions_wards")


ward_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "ward_population")

ward_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
ward_pops <- ward_pops %>%
  mutate(year2 = year+0.5)

#Get the Division population
division_pops <- ward_pops %>%
  group_by(division, year) %>%
  summarise(population_without_inst_ship = sum(population_without_inst_ship, na.rm = TRUE),
            institutions = sum(institutions, na.rm = TRUE),
            shipping = sum(shipping, na.rm = TRUE),
            total_population = sum(total_population, na.rm = TRUE))

`summarise()` has grouped output by 'division'. You can override using the `.groups` argument.

division_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

NA

Plot the overall population by Division and Ward

division_pops %>%
  mutate(year2 = year+0.5) %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2, colour=division, fill=division), alpha=0.8) +
  geom_point(aes(y=total_population, x=year2, colour=division)) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  facet_wrap(division~.) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_brewer(palette = "Set3", name = "") +
  scale_colour_brewer(palette = "Set3", name = "") +
  labs(
    title = "Glasgow Corporation: total population by Division",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

NA
NA

ward_pops %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2, colour=division, fill=division)) +
  geom_point(aes(y=total_population, x=year2, colour=division), colour="black") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  facet_wrap(ward~., ncol=6) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_brewer(palette = "Set3", name="Division") +
  scale_colour_brewer(palette = "Set3", name = "Division") +
  labs(
    x = "",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

ggsave(here("figures/s3.tiff"), height=14, width=12)

Approximately, how many person-years of follow-up do we have?

overall_pops %>%
  ungroup() %>%
  summarise(across(year, length, .names = "years"),
            across(c(population_without_inst_ship, total_population), sum)) %>%
  mutate(across(where(is.double), comma)) %>%
  datatable()

NA
NA

Change in population by ward

ward_pops %>%
  group_by(ward) %>%
  summarise(pct_change_pop = (last(population_without_inst_ship) - first(population_without_inst_ship))/first(population_without_inst_ship)) %>%
  mutate(pct_change_pop = percent(pct_change_pop)) %>%
  arrange(pct_change_pop) %>%
  datatable()

NA
NA
NA

3.3 Population by age and sex

age_sex <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "age_sex_population") %>%
  pivot_longer(cols = c(male, female),
               names_to = "sex")

#collapse down to smaller age groups to be manageable
age_sex <- age_sex %>%
  ungroup() %>%
  mutate(age = case_when(age == "0 to 4" ~ "00 to 04",
                         age == "5 to 9" ~ "05 to 14",
                         age == "10 to 14" ~ "05 to 14",
                         age == "15 to 19" ~ "15 to 24",
                         age == "20 to 24" ~ "15 to 24",
                         age == "25 to 29" ~ "25 to 34",
                         age == "30 to 34" ~ "25 to 34",
                         age == "35 to 39" ~ "35 to 44",
                         age == "40 to 44" ~ "35 to 44",
                         age == "45 to 49" ~ "45 to 59",
                         age == "50 to 54" ~ "45 to 59",
                         age == "55 to 59" ~ "45 to 59",
                         TRUE ~ "60 & up")) %>%
  group_by(year, age, sex) %>%
  mutate(value = sum(value)) %>%
  ungroup()



m_age_sex <- lm(value ~ splines::ns(year, knots = 3)*age*sex, data = age_sex)

summary(m_age_sex)

Warning: essentially perfect fit: summary may be unreliable

Call:
lm(formula = value ~ splines::ns(year, knots = 3) * age * sex, 
    data = age_sex)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.107e-10 -7.560e-13  0.000e+00  0.000e+00  2.107e-10 

Coefficients: (14 not defined because of singularities)
                                                    Estimate Std. Error    t value Pr(>|t|)    
(Intercept)                                        5.222e+04  3.820e-10  1.367e+14   <2e-16 ***
splines::ns(year, knots = 3)1                     -8.043e+03  7.621e-10 -1.055e+13   <2e-16 ***
splines::ns(year, knots = 3)2                             NA         NA         NA       NA    
age05 to 14                                        3.669e+04  4.679e-10  7.843e+13   <2e-16 ***
age15 to 24                                       -3.893e+03  4.679e-10 -8.320e+12   <2e-16 ***
age25 to 34                                       -3.996e+04  4.679e-10 -8.540e+13   <2e-16 ***
age35 to 44                                       -4.230e+04  4.679e-10 -9.040e+13   <2e-16 ***
age45 to 59                                        5.459e+04  4.411e-10  1.238e+14   <2e-16 ***
age60 & up                                         7.533e+04  4.126e-10  1.826e+14   <2e-16 ***
sexmale                                            3.374e+03  5.402e-10  6.244e+12   <2e-16 ***
splines::ns(year, knots = 3)1:age05 to 14         -1.863e+03  9.334e-10 -1.996e+12   <2e-16 ***
splines::ns(year, knots = 3)2:age05 to 14                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age15 to 24          7.533e+04  9.334e-10  8.070e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age15 to 24                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age25 to 34          1.325e+05  9.334e-10  1.420e+14   <2e-16 ***
splines::ns(year, knots = 3)2:age25 to 34                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age35 to 44          1.380e+05  9.334e-10  1.479e+14   <2e-16 ***
splines::ns(year, knots = 3)2:age35 to 44                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age45 to 59          3.474e+03  8.800e-10  3.948e+12   <2e-16 ***
splines::ns(year, knots = 3)2:age45 to 59                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age60 & up          -8.453e+04  8.232e-10 -1.027e+14   <2e-16 ***
splines::ns(year, knots = 3)2:age60 & up                  NA         NA         NA       NA    
splines::ns(year, knots = 3)1:sexmale             -1.994e+03  1.078e-09 -1.850e+12   <2e-16 ***
splines::ns(year, knots = 3)2:sexmale                     NA         NA         NA       NA    
age05 to 14:sexmale                                1.053e+04  6.617e-10  1.592e+13   <2e-16 ***
age15 to 24:sexmale                                2.352e+04  6.617e-10  3.555e+13   <2e-16 ***
age25 to 34:sexmale                                1.355e+04  6.617e-10  2.047e+13   <2e-16 ***
age35 to 44:sexmale                               -1.727e+03  6.617e-10 -2.611e+12   <2e-16 ***
age45 to 59:sexmale                                2.774e+03  6.238e-10  4.446e+12   <2e-16 ***
age60 & up:sexmale                                -7.761e+04  5.835e-10 -1.330e+14   <2e-16 ***
splines::ns(year, knots = 3)1:age05 to 14:sexmale -2.049e+04  1.320e-09 -1.552e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age05 to 14:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age15 to 24:sexmale -6.780e+04  1.320e-09 -5.136e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age15 to 24:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age25 to 34:sexmale -3.804e+04  1.320e-09 -2.882e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age25 to 34:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age35 to 44:sexmale -1.171e+04  1.320e-09 -8.874e+12   <2e-16 ***
splines::ns(year, knots = 3)2:age35 to 44:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age45 to 59:sexmale -3.473e+04  1.244e-09 -2.791e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age45 to 59:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age60 & up:sexmale   1.056e+05  1.164e-09  9.071e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age60 & up:sexmale          NA         NA         NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.755e-11 on 44 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 1.714e+29 on 27 and 44 DF,  p-value: < 2.2e-16

age_levels <- age_sex %>% select(age) %>% distinct() %>% pull() 

age_sex_nd <- 
  crossing(
    age=age_levels,
    sex=c("male", "female"),
    year = 1950:1963
  )

pred_pops <- age_sex_nd %>% modelr::add_predictions(m_age_sex)

Warning: prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases

pred_pops %>%
  ggplot(aes(x=year, y=pred, colour=age)) +
  geom_line() +
  geom_point() +
  facet_grid(sex~.) +
  scale_y_continuous(labels = comma, limits = c(0, 125000))

#How well do they match up with our overall populations?
pred_pops %>%
  group_by(year) %>%
  summarise(sum_pred_pop = sum(pred)) %>%
  right_join(overall_pops) %>%
  select(year, sum_pred_pop, population_without_inst_ship, total_population) %>%
  pivot_longer(cols = c(sum_pred_pop, population_without_inst_ship, total_population)) %>%
  ggplot(aes(x=year, y=value, colour=name)) +
  geom_point() +
  scale_y_continuous(labels = comma, limits = c(800000, 1250000))

Joining with `by = join_by(year)`

pred_pops %>%
  group_by(year, sex) %>%
  summarise(sum = sum(pred)) %>%
  group_by(year) %>%
  mutate(sex_ratio = first(sum)/last(sum))

`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

Population pyramids

label_abs <- function(x) {
  comma(abs(x))
}


pred_pops %>%
  ungroup() %>%
  group_by(year) %>%
  mutate(year_pop = sum(pred),
         age_sex_pct = percent(pred/year_pop, accuracy=0.1)) %>%
  mutate(sex = case_when(sex=="male" ~ "Male",
                         sex=="female" ~ "Female")) %>%
  ggplot(
    aes(x = age, fill = sex, 
        y = ifelse(test = sex == "Female",yes = -pred, no = pred))) + 
  geom_bar(stat = "identity") +
  geom_text(aes(label = age_sex_pct),
            position= position_stack(vjust=0.5), colour="black", size=2.5) +
  facet_wrap(year~., ncol=7) +
  coord_flip() +
  scale_y_continuous(labels = label_abs) +
  scale_fill_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  theme_ggdist() +
  theme(axis.text.x = element_text(angle=90, hjust = 1, vjust=0.5),
        legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="", y="") 


ggsave(here("figures/s4.tiff"), width=10)

Saving 10 x 4.51 in image

Not perfect, but resonably good. But ahhhhh… the age groups don’t align with the case notification age groups! Come back to think about this later.

4. Tuberculosis cases

Import the tuberculosis cases dataset

4.1 Overall notifications

Overall, by year.

cases_by_year <- read_xlsx("2023-11-28_glasgow-acf.xlsx", sheet = "by_year")

cases_by_year%>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
cases_by_year <- cases_by_year %>%
  mutate(year2 = year+0.5)

Plot the overall number of case notified per year, by pulmonary and extra pulmonary classification.

cases_by_year %>%
  select(-total_notifications, -year) %>%
  pivot_longer(cols = c(pulmonary_notifications, `non-pulmonary_notifications`)) %>%
  mutate(name = case_when(name == "pulmonary_notifications" ~ "Pulmonary TB",
                          name == "non-pulmonary_notifications" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

NA
NA

4.2 Notifications by Division

Read in the datasets and merge together.

#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")

#get the ward sheets
ward_sheets <- enframe(all_sheets) %>%
  filter(grepl("by_ward", value)) %>%
  pull(value)


cases_by_ward_sex_year <- map_df(ward_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

cases_by_ward_sex_year %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

NA

Aggregate together to get cases by division

cases_by_division <- cases_by_ward_sex_year %>%
  left_join(ward_lookup) %>%
  group_by(division, year, tb_type) %>%
  summarise(cases = sum(cases, na.rm = TRUE))

Joining with `by = join_by(ward)``summarise()` has grouped output by 'division', 'year'. You can override using the `.groups` argument.

#shift year to midpoint
cases_by_division <- cases_by_division %>%
  mutate(year2 = year+0.5) %>%
  ungroup()

cases_by_division  %>%
  select(-year2) %>%
  select(year, everything()) %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

cases_by_division %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(division~., scales = "free_y") +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications by Division",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

4.3 Notifications by ward

cases_by_ward <- cases_by_ward_sex_year %>%
  group_by(ward, year, tb_type) %>%
  summarise(cases = sum(cases, na.rm = TRUE)) %>%
  ungroup()

`summarise()` has grouped output by 'ward', 'year'. You can override using the `.groups` argument.

cases_by_ward %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  select(year, everything()) %>%
  datatable()

#shift year to midpoint
cases_by_ward <- cases_by_ward %>%
  mutate(year2 = year+0.5)

cases_by_ward %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.8) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(ward~., scales = "free_y") +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications by Ward",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme(legend.position = "bottom")

NA
NA

4.4 Notifications by age and sex

As we don’t have denominators, we will just model the change in counts.

#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")

#get the ward sheets
age_sex_sheets <- enframe(all_sheets) %>%
  filter(grepl("by_age_sex", value)) %>%
  pull(value)


cases_by_age_sex <- map_df(age_sex_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

cases_by_age_sex %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

NA
NA

4.5 Uptake of screening

What percentage of adults (15+ participated in the intervention in 1957)?

Note that in the Report of Sir Kenneth Cowan, we have the following estimates of participation (we will use these for the manuscript, as they are not based on my estimates)

male_adult_resident_participation <- 281875
female_adult_resident_participation <- 340474
male_adult_resident_population <- 381713
female_adult_resident_population <- 437588

#overall participation
(male_adult_resident_participation+female_adult_resident_participation)/(male_adult_resident_population+female_adult_resident_population)

[1] 0.7596097

#male participation
male_adult_resident_participation/male_adult_resident_population

[1] 0.7384475

#female participation
female_adult_resident_participation/female_adult_resident_population

[1] 0.7780698

Look at uptake of screening by age and sex

uptake_age_sex <- read_xlsx("2024-03-26_mass_xray_uptake.xlsx", sheet = "uptake_age_sex")

uptake_graph <- uptake_age_sex %>%
  mutate(uptake = examined/resident_population) %>%
  mutate(examined_l = comma(examined),
         resident_population_l = comma(resident_population),
         uptake_l = percent(uptake, accuracy=0.1)) %>%
  mutate(label = glue("{examined_l}/{resident_population_l} ({uptake_l})")) %>%
  filter(age !="00-14") %>%
  mutate(sex = case_when(sex=="m" ~ "Male",
                         sex=="f" ~ "Female")) %>%
  ggplot(aes(x=age, y=uptake, group=sex, fill=sex)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_text(aes(label=uptake_l), position = position_dodge(width=0.85),vjust=2) +
  scale_y_continuous(labels=percent) +
  scale_fill_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="", y="")

Combine figure with table for single figure.

uptake_table <- uptake_age_sex %>%
  mutate(resident_population = comma(resident_population),
         examined = comma(examined)) %>%
  rename(Sex = sex,
         Age = age,
         `Resident population` = resident_population,
         Examined = examined) %>%
  mutate(Sex = case_when(Sex=="m" ~ "Male",
                         Sex=="f" ~ "Female"))

uptake_graph / gridExtra::tableGrob(uptake_table, rows = NULL)

ggsave(here("figures/s5.tiff"), height=10)

Saving 7.29 x 10 in image

Uptake by division

uptake_division <- read_xlsx("2024-03-26_mass_xray_uptake.xlsx", sheet = "uptake_division")

division_pops %>%
  filter(year==1957) %>%
  select(division, population_without_inst_ship) %>%
  left_join(uptake_division) %>%
  mutate(pct_pop_examined = examined/population_without_inst_ship)

Joining with `by = join_by(division)`

5 TB case notification rates

5.1 Overall TB case notification rates

Now calculate case notification rates per 100,000 population

Merge the notification and population denominator datasets together.

Here we need to include the whole population (with shipping and institutions) as they are included in the notifications.

overall_inc <- overall_pops %>%
  left_join(cases_by_year)

Joining with `by = join_by(year, year2)`

overall_inc <- overall_inc %>%
  mutate(inc_pulm_100k = pulmonary_notifications/total_population*100000,
         inc_ep_100k = `non-pulmonary_notifications`/total_population*100000,
         inc_100k = total_notifications/total_population*100000)

overall_inc %>%
  select(year, inc_100k, inc_pulm_100k, inc_ep_100k) %>%
  mutate_at(.vars = vars(inc_100k, inc_pulm_100k, inc_ep_100k),
            .funs = funs(round)) %>%
  datatable()

Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

overall_inc %>%
  select(year2, inc_pulm_100k, inc_ep_100k) %>%
  pivot_longer(cols = c(inc_pulm_100k, `inc_ep_100k`)) %>%
  mutate(name = case_when(name == "inc_pulm_100k" ~ "Pulmonary TB",
                          name == "inc_ep_100k" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

NA
NA
NA

Change in case notification rates pre-intervention

#pre-ACF
overall_inc %>%
  filter(year %in% 1950:1956) %>%
  summarise(change = (((last(inc_pulm_100k)-first(inc_pulm_100k))/first(inc_pulm_100k))/7)*100)

#post-ACF
overall_inc %>%
  filter(year %in% 1958:1963) %>%
  summarise(change = (((last(inc_pulm_100k)-first(inc_pulm_100k))/first(inc_pulm_100k))/6)*100)

NA

5.2 TB case notification rates by Division

division_inc <- division_pops %>%
  left_join(cases_by_division)

Joining with `by = join_by(division, year)`

division_inc <- division_inc %>%
  mutate(inc_100k = cases/total_population*100000)

division_inc %>%
  select(year, division, tb_type, inc_100k) %>%
  mutate_at(.vars = vars(inc_100k),
            .funs = funs(round)) %>%
  datatable()

Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

division_inc %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(division~.) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate, by Division",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

NA
NA
NA

5.2 TB case notification rates by Ward

Here we will filter out the institutions and harbour from the denominators, as we don’t have reliable population denominators for them.

ward_inc <- ward_pops %>%
  left_join(cases_by_ward)

Joining with `by = join_by(ward, year, year2)`

ward_inc <- ward_inc %>%
  mutate(inc_100k = cases/population_without_inst_ship*100000)

ward_inc %>%
  select(year, ward, tb_type, inc_100k) %>%
  mutate_at(.vars = vars(inc_100k),
            .funs = funs(round)) %>%
  datatable()

Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

ward_inc %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(ward~.) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate, by Ward",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Incidence (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme(legend.position = "bottom")

NA
NA
NA
NA

On a map

st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
  filter(tb_type=="Pulmonary") %>%
  ggplot() +
  geom_sf(aes(fill=inc_100k)) +
  facet_wrap(year~., ncol = 7) +
  scale_fill_viridis_c(name="Case notification rate (per 100,000)",
                       option = "A") +
  theme_ggdist() +
  theme(legend.position = "top",
        legend.key.width = unit(2, "cm"),
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  guides(fill=guide_colorbar(title.position = "top"))

Joining with `by = join_by(division, ward, ward_number)`

6. TB Mortality

6.1 Overall Mortality

Import the TB mortality data.

First, overall deaths. Note that in the original reports, we have a pulmonary TB death rate per million for all years, and numbers of pulmonary TB deaths for each year apart from 1950.

#get the overall mortality sheets
deaths_sheets <- enframe(all_sheets) %>%
  filter(grepl("deaths", value)) %>%
  pull(value)


overall_deaths <- map_df(deaths_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

overall_deaths %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

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Plot the raw numbers of pulmonary deaths

overall_deaths %>%
  ggplot(aes(x=year, y=pulmonary_deaths)) +
  geom_line(colour = "#DE0D92") +
  geom_point(colour = "#DE0D92") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  labs(y="Pulmonary TB deaths per year",
       x = "Year",
       title = "Numbers of pulmonary TB deaths",
       subtitle = "Glasgow, 1950-1963",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: no data for 1950") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))

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Now the incidence of pulmonary TB death

overall_deaths %>%
  ggplot(aes(x=year, y=pulmonary_death_rate_per_100k)) +
  geom_line(colour = "#4D6CFA") +
  geom_point(colour = "#4D6CFA") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(y="Annual incidence of death (per 100,000)",
       x = "Year",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))

ggsave(here("figures/deaths.tiff"), width=10)

Saving 10 x 4.51 in image

6. Table 1

Make Table 1 here, and save for publication.

overall_pops %>% 
  select(year, total_population) %>%
  left_join(overall_inc %>%
              select(year, 
                     pulmonary_notifications, inc_pulm_100k,
                     `non-pulmonary_notifications`, inc_ep_100k,
                     total_notifications, inc_100k)) %>%
  left_join(overall_deaths %>%
              select(year,
                     pulmonary_deaths, pulmonary_death_rate_per_100k)) %>%
  mutate(percent_pulmonary = percent(pulmonary_notifications/(total_notifications ), accuracy=0.1)) %>%
  mutate(across(where(is.numeric) & !(year),  ~round(., digits=1))) %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.)))

Joining with `by = join_by(year)`Joining with `by = join_by(year)`

Comparison fo age-sex distribution of cases in 1950-1956 vs. 1957

label_abs2 <- function(x) {
  percent(abs(x))
}



cases_by_age_sex %>% 
  ungroup() %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(acf_period, age, sex) %>%
  summarise(cases = sum(cases)) %>%
  ungroup() %>%
  group_by(acf_period) %>%
  mutate(period_total = sum(cases)) %>%
  mutate(pct = cases/period_total) %>%
  mutate(pct2 = case_when(sex=="F" ~ -pct,
                          TRUE ~ pct)) %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
                 mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Pre-ACF",
                                               acf_period=="b. acf" ~ "ACF",
                                               acf_period=="c. post-acf" ~ "Post-ACF")) %>%
  ggplot() +
  geom_vline(aes(xintercept=0), linetype=2) +
  geom_point(aes(x=pct2,y=age, colour=fct_relevel(acf_period,
                                                             "Pre-ACF",
                                                             "ACF",
                                                             "Post-ACF")), stat="identity") +
  scale_x_continuous(labels=label_abs2, limits = c(-0.2, 0.2)) +
  scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA")) +
  theme_grey(base_family = "Aptos") +
  labs(x= "<- Female                Percent of cases              Male ->",
       y="") +
  theme(legend.title = element_blank(),
        legend.position = "bottom")

`summarise()` has grouped output by 'acf_period', 'age'. You can override using the `.groups` argument.

ggsave(here("figures/s6.tiff"))

Saving 7.29 x 4.51 in image

Prepare the datasets for modelling

mdata <- ward_inc %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(ward) %>%
  mutate(y_num = row_number()) %>%
  ungroup()


mdata_extrapulmonary <- ward_inc %>%
  filter(tb_type=="Non-Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(ward) %>%
  mutate(y_num = row_number()) %>%
  ungroup() %>% 
  filter(year<=1961) #no data for 1962 and 1963


#scaffold for overall predictions
overall_scaffold <- mdata %>%
    select(year, year2, y_num, acf_period, population_without_inst_ship, ward, cases) %>%
    group_by(year, year2, y_num, acf_period) %>%
    summarise(population_without_inst_ship = sum(population_without_inst_ship),
              cases = sum(cases)) %>%
    ungroup() %>%
    mutate(inc_100k = cases/population_without_inst_ship*100000) %>%
    left_join(mdata_extrapulmonary %>% group_by(year) %>%
                summarise(cases_extrapulmonary = sum(cases))) %>%
    mutate(inc_100k_extrapulmonary = cases_extrapulmonary/population_without_inst_ship*100000)

`summarise()` has grouped output by 'year', 'year2', 'y_num'. You can override using the `.groups` argument.Joining with `by = join_by(year)`

7. Pulmonary TB model

7.1 Fit the model

Look at the mean and variance of counts (counts of pulmonary notifications are what we are predicting)

#Mean of counts per year
mean(mdata$cases)

[1] 48.32819

#variance of counts per year
var(mdata$cases)

[1] 915.5749

Quite a bit of over-dispersion here, so negative binomial distribution might be a better choice of distributional family than Poisson.

Fit the model with the data

m_pulmonary <- brm(
  cases ~ 0 + Intercept + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)),
                  data = mdata,
                  family = negbinomial(),
                  seed = 1234,
                  threads = threading(2, grainsize = 100, static = TRUE), #for exact reproducibility
                  backend = "cmdstanr",
                  chains = 4, cores = 4,
                  prior = prior(normal(0,1), class=b, coef = "Intercept") +
                          prior(gamma(0.01, 0.01), class = shape) +
                          prior(normal(0, 1), class = b) +
                          prior(exponential(1), class=sd) +
                          prior(lkj(4), class=cor),
  control = list(adapt_delta = 0.9))

Compiling Stan program...

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Loading required package: rstan
Loading required package: StanHeaders
Warning: package ‘StanHeaders’ was built under R version 4.3.3
rstan version 2.32.6 (Stan version 2.32.2)

For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
change `threads_per_chain` option:
rstan_options(threads_per_chain = 1)


Attaching package: ‘rstan’

The following object is masked from ‘package:tidyr’:

    extract

  
#check model diagnostics
summary(m_pulmonary)

 Family: negbinomial 
  Links: mu = log; shape = identity 
Formula: cases ~ 0 + Intercept + y_num * acf_period + (1 + y_num * acf_period | ward) + offset(log(population_without_inst_ship)) 
   Data: mdata (Number of observations: 518) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Multilevel Hyperparameters:
~ward (Number of levels: 37) 
                                                      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)                                             0.25      0.03     0.19     0.33 1.00     1041     2023
sd(y_num)                                                 0.02      0.01     0.01     0.03 1.00      631      579
sd(acf_periodb.acf)                                       0.06      0.04     0.00     0.16 1.00     1506     1729
sd(acf_periodc.postMacf)                                  0.12      0.06     0.01     0.24 1.00      775     1331
sd(y_num:acf_periodb.acf)                                 0.01      0.01     0.00     0.02 1.00     1167     1599
sd(y_num:acf_periodc.postMacf)                            0.01      0.01     0.00     0.02 1.00      521     1213
cor(Intercept,y_num)                                     -0.45      0.19    -0.75    -0.02 1.00     1316     1423
cor(Intercept,acf_periodb.acf)                           -0.23      0.29    -0.70     0.38 1.00     2722     2557
cor(y_num,acf_periodb.acf)                               -0.04      0.27    -0.55     0.50 1.00     4075     3006
cor(Intercept,acf_periodc.postMacf)                      -0.14      0.24    -0.57     0.38 1.00     2896     2380
cor(y_num,acf_periodc.postMacf)                           0.10      0.25    -0.42     0.57 1.00     2233     2626
cor(acf_periodb.acf,acf_periodc.postMacf)                 0.06      0.27    -0.47     0.56 1.00     1953     2391
cor(Intercept,y_num:acf_periodb.acf)                     -0.24      0.28    -0.71     0.36 1.00     2295     2772
cor(y_num,y_num:acf_periodb.acf)                         -0.04      0.26    -0.52     0.47 1.00     5057     3047
cor(acf_periodb.acf,y_num:acf_periodb.acf)               -0.05      0.29    -0.59     0.50 1.00     4064     3022
cor(acf_periodc.postMacf,y_num:acf_periodb.acf)           0.07      0.27    -0.49     0.56 1.00     3179     3431
cor(Intercept,y_num:acf_periodc.postMacf)                -0.02      0.26    -0.52     0.49 1.00     3955     2979
cor(y_num,y_num:acf_periodc.postMacf)                    -0.03      0.28    -0.56     0.52 1.00     2265     3079
cor(acf_periodb.acf,y_num:acf_periodc.postMacf)           0.05      0.28    -0.51     0.56 1.00     2269     2897
cor(acf_periodc.postMacf,y_num:acf_periodc.postMacf)     -0.08      0.29    -0.62     0.49 1.00     2520     2895
cor(y_num:acf_periodb.acf,y_num:acf_periodc.postMacf)     0.06      0.28    -0.50     0.58 1.00     1895     2833

Regression Coefficients:
                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept                     -6.14      0.05    -6.23    -6.04 1.00      795     1674
y_num                         -0.02      0.01    -0.03    -0.01 1.00     2412     2841
acf_periodb.acf                0.01      1.00    -1.98     1.91 1.00     3260     2784
acf_periodc.postMacf           0.04      0.11    -0.17     0.25 1.00     2944     3110
y_num:acf_periodb.acf          0.08      0.12    -0.16     0.33 1.00     3281     2725
y_num:acf_periodc.postMacf    -0.05      0.01    -0.07    -0.03 1.00     2649     2847

Further Distributional Parameters:
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape    92.15     20.99    60.74   142.29 1.00     2682     2250

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

plot(m_pulmonary)

pp_check(m_pulmonary, type='ecdf_overlay')

Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

prior_summary(m_pulmonary)

                prior class                       coef group resp dpar nlpar lb ub       source
         normal(0, 1)     b                                                                user
         normal(0, 1)     b            acf_periodb.acf                             (vectorized)
         normal(0, 1)     b       acf_periodc.postMacf                             (vectorized)
         normal(0, 1)     b                  Intercept                                     user
         normal(0, 1)     b                      y_num                             (vectorized)
         normal(0, 1)     b      y_num:acf_periodb.acf                             (vectorized)
         normal(0, 1)     b y_num:acf_periodc.postMacf                             (vectorized)
 lkj_corr_cholesky(4)     L                                                                user
 lkj_corr_cholesky(4)     L                             ward                       (vectorized)
       exponential(1)    sd                                                   0            user
       exponential(1)    sd                             ward                  0    (vectorized)
       exponential(1)    sd            acf_periodb.acf  ward                  0    (vectorized)
       exponential(1)    sd       acf_periodc.postMacf  ward                  0    (vectorized)
       exponential(1)    sd                  Intercept  ward                  0    (vectorized)
       exponential(1)    sd                      y_num  ward                  0    (vectorized)
       exponential(1)    sd      y_num:acf_periodb.acf  ward                  0    (vectorized)
       exponential(1)    sd y_num:acf_periodc.postMacf  ward                  0    (vectorized)
    gamma(0.01, 0.01) shape                                                   0            user

Nicer version of trace plots for supplemental material

as_draws_df(m_pulmonary) %>% 
  bayesplot::mcmc_rank_overlay(pars = vars(b_Intercept:shape),
             facet_args = list(ncol = 4)) +
  scale_colour_scico_d(palette = "managua", name = "Chain") +
  theme_ggdist()+
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        legend.position = "top")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

ggsave(here("figures/s8.tiff"), width=16, height=16)

Nicer version of table of parameters for supplement

summarise_draws(m_pulmonary) %>%
  mutate(across(c(mean:ess_tail), comma, accuracy=0.01)) %>%
  write_csv(here("figures/s9_table.csv"))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `across(c(mean:ess_tail), comma, accuracy = 0.01)`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.

  # Previously
  across(a:b, mean, na.rm = TRUE)

  # Now
  across(a:b, \(x) mean(x, na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

7.2 Summarise change in CNRs

Summarise the posterior in graphical form

f1b <- plot_counterfactual(model_data = overall_scaffold, model = m_pulmonary, 
                           population_denominator = population_without_inst_ship, outcome = inc_100k, grouping_var=NULL,
                           re_formula = NA)
  
f1b

Make this into a figure combined with the map of empirical data

f1a <- st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
  filter(tb_type=="Pulmonary") %>%
  ggplot() +
  geom_sf(aes(fill=inc_100k), colour="grey98", lwd=0.01) +
  facet_wrap(year~., ncol = 7) +
  scale_fill_scico(name="CNR (per 100,000)",
                       palette = "acton", direction = -1) +
  theme_grey() +
  theme(legend.position = "top",
        #legend.key.width = unit(1, "cm"),
        legend.title.align = 0.5,
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        panel.background = element_blank(),
        legend.title = element_text(size=10))

Joining with `by = join_by(division, ward, ward_number)`Warning: The `legend.title.align` argument of `theme()` is deprecated as of ggplot2 3.5.0.
Please use theme(legend.title = element_text(hjust)) instead.

(f1a / f1b) + plot_annotation(tag_levels = "A")

ggsave(here("figures/f1.tiff"), width=7, height=8)

Summary of change in notifications numerically

overall_change <- summarise_change(model_data=overall_scaffold, model=m_pulmonary, 
                                   population_denominator=population_without_inst_ship, grouping_var=NULL, re_formula = NA)

`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.

#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
overall_change %>%
  keep((names(.) %in% tokeep)) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()

NA
NA

7.3 Compared to counterfactual

Numbers of pulmonary TB cases averted compared to counterfactual per year.

overall_pulmonary_counterf <- calculate_counterfactual(model_data = overall_scaffold, model=m_pulmonary, population_denominator = population_without_inst_ship)

Joining with `by = join_by(year, population_without_inst_ship, .draw)`Joining with `by = join_by(.draw)`

overall_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()

NA
NA

Total pulmonary TB cases averted between 1958 and 1963

overall_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()

NA
NA

7.4 Correlation between RR.peak, RR.level, and RR.slope

What are the correlations between peak, level, and slope?

#RR.peak histogram
a <- overall_change$peak_draws %>%
  ggplot() +
  geom_histogram(aes(x=estimate), fill="darkblue", colour="darkblue", alpha=0.3)+
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="RR.peak",
       y="")

#RR. level histogram
b <- overall_change$level_draws  %>%
  ggplot() +
  geom_histogram(aes(x=estimate), fill="darkblue", colour="darkblue", alpha=0.3)+
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="RR.level",
       y="")

#RR.slope histogram
c <- overall_change$slope_draws %>%
  ggplot() +
  geom_histogram(aes(x=estimate), fill="darkblue", colour="darkblue", alpha=0.3)+
  scale_fill_gradient(high="lightblue1",low="darkblue") +  
  #scale_x_continuous(limits = c(0, 6)) +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="RR.slope",
       y="")


#Correlation between RR.peak and RR.level
cor_rr_peak_rr_level <- round(cor(pluck(overall_change$peak_draws$estimate), pluck(overall_change$level_draws$estimate)), digits = 2)

#Correlation between RR.peak and RR.slope
cor_rr_peak_rr_slope <- round(cor(pluck(overall_change$peak_draws$estimate), pluck(overall_change$slope_draws$estimate)), digits = 2)

#Correlation between RR.level and RR.slope
cor_rr_level_rr_slope <- round(cor(pluck(overall_change$level_draws$estimate), pluck(overall_change$slope_draws$estimate)), digits = 2)


#plot of correlation between RR.peak and RR.level
d <- bind_cols(RR.peak=pluck(overall_change$peak_draws$estimate), 
          RR.level =pluck(overall_change$level_draws$estimate)) %>%
  ggplot(aes(y=RR.peak, x = RR.level)) +
  geom_hex() +
  geom_smooth(se=FALSE, colour="firebrick", method = "lm") +
  geom_text(aes(y=2.2, x=0.58, label=cor_rr_peak_rr_level), colour="firebrick")  +
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))

#plot of correlation between RR.peak and RR.slope
e <- bind_cols(RR.peak=pluck(overall_change$peak_draws$estimate), 
          RR.slope =pluck(overall_change$slope_draws$estimate)) %>%
  ggplot(aes(y=RR.peak, x = RR.slope)) +
  geom_hex() +
  geom_smooth(se=FALSE, colour="firebrick", method = "lm") +
  geom_text(aes(y=2.1, x=0.65, label=cor_rr_peak_rr_slope), colour="firebrick")  +
  #scale_x_continuous(limits = c(0, 6)) +
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))

#plot of correlation between RR.level and RR.slope
f <- bind_cols(RR.level=pluck(overall_change$level_draws$estimate), 
          RR.slope =pluck(overall_change$slope_draws$estimate)) %>%
  ggplot(aes(y=RR.level, x = RR.slope)) +
  geom_hex() +
  geom_smooth(se=FALSE, colour="firebrick", method = "lm") +
  geom_text(aes(y=0.75, x=0.65, label=cor_rr_level_rr_slope), colour="firebrick")  +  
  #scale_x_continuous(limits = c(0, 6)) +
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))


(plot_spacer() + plot_spacer() + c) /
  (plot_spacer() + b + f) /
  (a + d + e)

ggsave(here("figures/s10.tiff"), width=8, height=8)

NA
NA
NA

7.5 Ward level pulmonary TB estimates

Plot the counterfactual at ward level

plot_counterfactual(model_data = mdata, model=m_pulmonary, outcome = inc_100k, population_denominator = population_without_inst_ship, 
                    grouping_var = ward, ward, re_formula= ~(1 + y_num*acf_period | ward)) +
  scale_y_continuous(limits = c(0,500))

Scale for y is already present.
Adding another scale for y, which will replace the existing scale.

  
ggsave(here("figures/s7.tiff"), width=16, height=12)

Summary of change in notifications at ward level

ward_change <- summarise_change(model_data=mdata, model=m_pulmonary, 
                                   population_denominator=population_without_inst_ship, grouping_var=ward, 
                                   re_formula = ~(1 + y_num*acf_period | ward))

`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw', 'y_num'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.

#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
ward_change %>%
  keep((names(.) %in% tokeep)) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()

#plot these in a figure
ward_effects <- ward_change %>%
  keep((names(.) %in% tokeep)) %>%
  bind_rows() %>%
  bind_rows(overall_change$peak_summary) %>%
  bind_rows(overall_change$level_summary) %>%
  bind_rows(overall_change$slope_summary) %>%
  mutate_at(.vars = vars(estimate:.upper), 
            .funs = funs(as.numeric)) %>%
  select(measure, everything()) %>%
  mutate(estimate = as.double(estimate)) %>%
  full_join(glasgow_wards_1951) %>% 
  mutate(ward2 = paste0(ward_number, ". ", ward)) %>%
  mutate(ward2 = case_when(is.na(ward) ~ "Overall",
                          TRUE ~ ward2)) %>%
  st_as_sf() 

Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))Joining with `by = join_by(ward)`

#function for plotting choropleth maps
plot_ward_effect <- function(data, measure){
  {{data}} %>%
  filter(measure == {{measure}}) %>%
  ggplot() +
  geom_sf(aes(fill=estimate)) +
  geom_sf_label(aes(label = ward_number), size=3, fill=NA, label.size = NA, colour="black") +
  scale_fill_scico(trans="log", palette = "roma", midpoint = 0, limits=c(0.5,2.25),
                       breaks = c(0.5, 0.75, 1, 1.5, 2, 2.5), labels = c(0.5, 0.75, 1, 1.5, 2, 2.5),
                   name="Relative rate") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        axis.text.x=element_text(angle=45, hjust=1)) +
    labs(x="", y="")
}

#function for plotting catapiller plots
plot_ward_cat <- function(data, measure, scale){

    ggplot() +
    geom_hline(aes(yintercept=1), linetype=2) +
    geom_pointrange(data = {{data}} %>%     
                      filter(measure=={{measure}}) %>%
                      filter(!is.na(ward)),
                    aes(y=estimate, ymin=.lower, ymax=.upper, 
                      x=fct_reorder(ward2, estimate), colour=estimate)) +
    geom_pointrange(data = {{data}} %>% 
                      filter(measure=={{measure}}) %>%
                      filter(is.na(ward)),
                    aes(y=estimate, ymin=.lower, ymax=.upper, 
                      x=ward2), colour="black") +
    coord_flip() +
    scale_colour_scico(trans="log", palette = "roma", midpoint = 0, limits=c(0.5,2.25), 
                       breaks = c(0.5, 0.75, 1, 1.5, 2, 2.5), labels = c(0.5, 0.75, 1, 1.5, 2, 2.5),
                       name="Relative rate") +
    scale_y_continuous() +
    theme_ggdist()  +
    theme(panel.border = element_rect(colour = "grey78", fill=NA)) +
    labs(x = "",
         y = "Relative rate (95% UI)")+
    guides(x = "axis_truncated", y = "axis_truncated")
}



ward_peak_i <- plot_ward_effect(data = ward_effects, measure = "RR.peak") + ggtitle("Peak effect")
ward_level_i <- plot_ward_effect(data = ward_effects, measure = "RR.level") + ggtitle("Level effect")
ward_slope_i <- plot_ward_effect(data = ward_effects, measure = "RR.slope") + ggtitle("Slope effect")

ward_peak_ii <- plot_ward_cat(data = ward_effects, measure = "RR.peak") + ggtitle("Peak effect")
ward_level_ii <- plot_ward_cat(data = ward_effects, measure = "RR.level") + ggtitle("Level effect")
ward_slope_ii <- plot_ward_cat(data = ward_effects, measure = "RR.slope") + ggtitle("Slope effect")

s4 <- (ward_peak_i + ward_level_i + ward_slope_i) /
  (ward_peak_ii + ward_level_ii + ward_slope_ii)

s4[[1]] <- s4[[1]] + plot_layout(tag_level = 'new')
s4[[2]] <- s4[[2]] + plot_layout(tag_level = 'new')
s4 + plot_annotation(tag_levels = c('A', '1')) + plot_layout(guides = 'collect') &
  theme(legend.position='bottom',
        legend.key.width = unit(3, "cm"))


ggsave(here("figures/f2.tiff"), width = 16, height=12)

Calculate the counterfactual per ward

ward_pulmonary_counterf <- calculate_counterfactual(model_data = mdata, model=m_pulmonary, 
                                                    population_denominator = population_without_inst_ship,
                                                    grouping_var = ward, re_formula=~(1 + y_num*acf_period | ward))

`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.Joining with `by = join_by(year, population_without_inst_ship, .draw, ward)`Joining with `by = join_by(.draw, ward)`

ward_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()

NA
NA

Overall counterfactual per ward

ward_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()

NA

8. Extra-pulmonary TB notifications

8.1 Fit the model

pp_check(m_extrapulmonary, type='ecdf_overlay')

Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

8.2 Summary of change

Summarise in plot

ggsave(here("figures/s11.tiff"), width=10)

Saving 10 x 7 in image

Summarise numerically.

8.3 Compared to counterfactual

Numbers of extra-pulmonary TB cases averted overall.

Total extrapulmonary TB cases averted between 1958 and 1963

Make into Table 2

bind_rows(
overall_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "PTB_ward"),

overall_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "PTB_overall"),

overall_ep_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "EPTB"),

overall_ep_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "EPTB overall")

) %>%
  select(model, year, diff_inc100k, diff_inc100k.lower:rr_inc100k.upper, 
         cases_averted:cases_averted.upper,
         pct_change:pct_change.upper) %>%
  transmute(model=model, year=year,
            diff_cnr = glue("{diff_inc100k} [{diff_inc100k.lower}, {diff_inc100k.upper}]"),
            rr = glue("{rr_inc100k} [{rr_inc100k.lower}, {rr_inc100k.upper}]"),
            cases_averted = glue("{cases_averted} [{cases_averted.lower}, {cases_averted.upper}]"),
            pct_change = glue("{pct_change} [{pct_change.lower}, {pct_change.upper}]")) %>%
  write_csv(here("figures/table2.csv"))

Error: object 'overall_ep_counterf' not found

8.4 Ward-level extra-pulmonary summaries

Ward-level extra-pulmonary estimates in graphical form.

plot_counterfactual(model_data = mdata_extrapulmonary, model=m_extrapulmonary, outcome = inc_100k, 
                    population_denominator = population_without_inst_ship, grouping_var = ward,re_formula =~(y_num*acf_period | ward), 
                    ward) + scale_y_continuous(limits= c(0,75))

Scale for y is already present.
Adding another scale for y, which will replace the existing scale.

Numerical summary.

9. Age-sex model

9.1 FIt the model

Fit the model

(Not rewritten the functions for this yet)

mdata_age_sex <- cases_by_age_sex %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  mutate(year2 = year+0.5) %>%
  group_by(age, sex) %>%
  mutate(y_num = row_number()) %>%
  ungroup()

m_age_sex <- brm(
  cases ~ y_num + (acf_period)*(age*sex) + (acf_period:y_num)*(age*sex),
                  data = mdata_age_sex,
                  family = negbinomial(),
                  seed = 1234,
                  threads = threading(2, grainsize = 100, static = TRUE), #for exact reproducibility
                  backend = "cmdstanr",
                  chains = 4, cores = 4, 
                  prior = prior(normal(0,1), class = Intercept) +
                          prior(gamma(0.01, 0.01), class = shape) +
                          prior(normal(0, 1), class = b))

summary(m_age_sex)
plot(m_age_sex)
pp_check(m_age_sex, type='ecdf_overlay')

Summarise posterior

#posterior draws, and summarise
age_sex_summary <- mdata_age_sex %>%
  select(year, year2, y_num, acf_period, age, sex) %>%
  add_epred_draws(m_age_sex) %>%
  group_by(year2, acf_period, age, sex) %>%
  mean_qi() %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))

#create the counterfactual (no intervention), and summarise
age_sex_counterfact <- 
  tibble(year = mdata_age_sex$year,
         year2 = mdata_age_sex$year2,
         y_num = mdata_age_sex$y_num,
         age = mdata_age_sex$age,
         sex = mdata_age_sex$sex,
         acf_period = factor("a. pre-acf")) %>%
  add_epred_draws(m_age_sex) %>%
  group_by(year2, acf_period, age, sex) %>%
  mean_qi() %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention")) %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) 



age_sex_summary %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) %>%
  ggplot() +
  geom_ribbon(aes(ymin=.epred.lower, ymax=.epred.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
  geom_ribbon(data = age_sex_counterfact %>% filter(year>=1956), 
              aes(ymin=.epred.lower, ymax=.epred.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
  geom_line(data = age_sex_counterfact %>% filter(year>=1956), 
              aes(y=.epred, x=year2, colour="Counterfactual")) +
  geom_line(aes(y=.epred, x=year2, group=acf_period,  colour=acf_period)) +
  geom_point(data = mdata_age_sex %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="b. acf" ~ "Counterfactual",
                                  acf_period=="c. post-acf" ~ "Post Intervention"))%>%
                 mutate(acf_period2 = case_when(acf_period=="Before Intervention" ~ "Pre-ACF",
                                               acf_period=="Counterfactual" ~ "ACF",
                                               acf_period=="Post Intervention" ~ "Post-ACF")), 
  aes(y=cases, x=year2, shape=fct_relevel(acf_period2,
                                                             "Pre-ACF",
                                                             "ACF",
                                                             "Post-ACF")), size=2) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  facet_grid(fct_relevel(age, "65 & up y", "56 to 65y", "46 to 55y", "36 to 45y", "26 to 35y", "16 to 25y", "06 to 15y", "0 to 5y")~sex, 
             scales = "free_y") +
  theme_ggdist() +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:") +
  scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:") +
  scale_shape_discrete(name="Emprical data (period):", na.translate = F) +
  labs(
    x = "Year",
    y = "Case notifications (n)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme(legend.position = "bottom",          
        legend.box="vertical", 
        panel.border = element_rect(colour = "grey78", fill=NA))
  
ggsave(here("figures/s14.tiff"), height=10)

9.2 Summary of impact of intervention

Calculate summary effects

#Peak
out_age_sex_1 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num == 8),
                      acf_period = c("a. pre-acf", "b. acf"))

peak_draws_age_sex <- add_epred_draws(newdata = out_age_sex_1,
                  object = m_age_sex) %>%
    group_by(.draw, age, sex) %>%
    summarise(estimate = last(.epred)/first(.epred)) %>%
    ungroup() %>%
    mutate(measure = "RR.peak")
  
peak_summary_age_sex <- peak_draws_age_sex %>%
    group_by(age, sex) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.peak")


#Level
 
out_age_sex_2 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num == 9),
                      acf_period = c("a. pre-acf", "c. post-acf"))
  
level_draws_age_sex <- add_epred_draws(newdata = out_age_sex_2,
                  object = m_age_sex) %>%
    arrange(y_num, .draw) %>%
    group_by(.draw, age, sex) %>%
    summarise(estimate = last(.epred)/first(.epred)) %>%
    ungroup() %>%
    mutate(measure = "RR.level")
  
level_summary_age_sex <- level_draws_age_sex %>%
    group_by(age, sex) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.level")

#Slope

out_age_sex_3 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf"))
  
slope_draws_age_sex <- add_epred_draws(newdata = out_age_sex_3,
                  object = m_age_sex) %>%
        arrange(y_num) %>%
        ungroup() %>%
        group_by(.draw, y_num, age, sex) %>%
        summarise(slope = last(.epred)/first(.epred)) %>%
        ungroup() %>%
        group_by(.draw, age, sex) %>%
        summarise(estimate = last(slope)/first(slope)) %>%
        mutate(measure = "RR.slope")
  
slope_summary_age_sex <- slope_draws_age_sex %>%
     group_by(age, sex) %>%
      median_qi(estimate) %>%
      mutate(measure = "RR.slope")

Numerical summary of these summary results

bind_rows(
  peak_summary_age_sex, level_summary_age_sex, slope_summary_age_sex
) %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()

As a figure

peak_g_age_sex <- peak_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

#level plot
level_g_age_sex <- level_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

#slope plot
slope_g_age_sex <- slope_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

9.3 Compared to counterfactual

counterfact_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex) %>%
                                    mutate(acf_period = "a. pre-acf")) %>%
      filter(year>1957) %>%
      select(year, age, sex, .draw, .epred_counterf = .epred)
  
#Calcuate predicted number of cases per draw, then summarise.
post_change_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex, acf_period)) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, age, sex, .draw, .epred) 
  
#for the overall period
counterfact_overall_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex) %>%
                                    mutate(acf_period = "a. pre-acf")) %>%
      filter(year>1957) %>%
      select(age, sex, .draw, .epred)  %>%
      group_by(age, sex, .draw) %>%
      summarise(.epred_counterf = sum(.epred)) %>%
      mutate(year = "Overall (1958-1963)")
  
#Calcuate incidence per draw, then summarise.
post_change_overall_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex, acf_period)) %>%
      filter(year>1957) %>%
      select(age, sex, .draw, .epred) %>%
      group_by(.draw, age, sex) %>%
      summarise(.epred = sum(.epred)) 
  
counter_post_overall_age_sex <-
  left_join(counterfact_overall_age_sex, post_change_overall_age_sex) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf) %>%
    group_by(age, sex) %>%
    mean_qi(cases_averted, pct_change) %>%
    ungroup() %>%
    mutate(year = "Overall (1958-1963)") 


age_sex_txt <- counter_post_overall_age_sex %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  transmute(year = as.character(year),
            sex = sex,
            age = age,
            cases_averted = glue::glue("{cases_averted}\n({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change}\n({pct_change.lower} to {pct_change.upper})"))


age_sex_txt %>% datatable()

counterfactual_g_age_sex <- counter_post_overall_age_sex %>% 
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_pointrange(aes(x = age, y=cases_averted, ymin=cases_averted.lower, ymax=cases_averted.upper, colour=sex, shape=sex), position=position_dodge(width=0.5)) + 
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  scale_y_continuous(labels = comma) +
  labs(x="",
       y="Number (95% UI)",
       colour="") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        legend.position = "bottom")

counterfactual_g_age_sex

Join together for Figure 3.

(peak_g_age_sex + level_g_age_sex) / (slope_g_age_sex + counterfactual_g_age_sex) + plot_annotation(tag_levels = "A") + plot_layout(guides = "collect") & theme(legend.position = "bottom")

ggsave(here("figures/f3.tiff"), width = 12, height=8)